/// <summary>
/// Return a full array with the same shape and type as a given array.
/// </summary>
/// <param name="a">The shape and data-type of a define these same attributes of the returned array.</param>
/// <param name="fill_value">Fill value.</param>
/// <param name="dtype">Overrides the data type of the result.</param>
/// <returns>Array of fill_value with the same shape and type as a.</returns>
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.full_like.html</remarks>
public static NDArray full_like(NDArray a, object fill_value, Type dtype = null)
{
var typeCode = (dtype ?? fill_value?.GetType() ?? a.dtype).GetTypeCode();
var shape = new Shape((int[])a.shape.Clone());
return(new NDArray(new UnmanagedStorage(ArraySlice.Allocate(typeCode, shape.size, Convert.ChangeType(fill_value, (TypeCode)typeCode)), shape)));
}
/// <summary>
/// Return a new array of given shape and type, filled with ones.
/// </summary>
/// <param name="shape">Shape of the new array.</param>
/// <param name="typeCode">The desired data-type for the array, e.g., <see cref="uint8"/>. Default is <see cref="float64"/> / <see cref="double"/>.</param>
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.ones.html</remarks>
public static NDArray ones(Shape shape, NPTypeCode typeCode)
{
object one = null;
switch (typeCode)
{
case NPTypeCode.Complex:
one = new Complex(1d, 0d);
break;
case NPTypeCode.NDArray:
one = NDArray.Scalar(1, np.int32);
break;
case NPTypeCode.String:
one = "1";
break;
case NPTypeCode.Char:
one = '1';
break;
default:
one = Converts.ChangeType((byte)1, typeCode);
break;
}
return(new NDArray(ArraySlice.Allocate(typeCode, shape.size, one), shape));
}
/// <summary>
/// Return a new array of given shape and type, filled with fill_value.
/// </summary>
/// <param name="fill_value">Fill value.</param>
/// <param name="shape">Shape of the empty array, e.g., (2, 3) or 2.</param>
/// <param name="typeCode">The desired data-type for the array The default, null, means np.array(fill_value).dtype.</param>
/// <returns>Array of fill_value with the given shape, dtype, and order.</returns>
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html</remarks>
public static NDArray full(ValueType fill_value, Shape shape, NPTypeCode typeCode)
{
if (typeCode == NPTypeCode.Empty)
{
throw new ArgumentNullException(nameof(typeCode));
}
return(new NDArray(new UnmanagedStorage(ArraySlice.Allocate(typeCode, shape.size, Convert.ChangeType(fill_value, (TypeCode)typeCode)), shape)));
}
/// <summary>
/// Clone internal storage and get reference to it
/// </summary>
/// <returns>reference to cloned storage as System.Array</returns>
public IArraySlice CloneData()
{
//Incase shape is not sliced, we can copy the internal buffer.
if (!_shape.IsSliced && !_shape.IsBroadcasted)
{
return(InternalArray.Clone());
}
if (_shape.IsScalar)
{
return(ArraySlice.Scalar(GetValue(0), _typecode));
}
//Linear copy of all the sliced items.
var ret = ArraySlice.Allocate(InternalArray.TypeCode, _shape.size, false);
MultiIterator.Assign(new UnmanagedStorage(ret, _shape.Clean()), this);
return(ret);
}
/// <summary>
/// Return a new array of given shape and type, filled with fill_value.
/// </summary>
/// <param name="fill_value">Fill value.</param>
/// <param name="shape">Shape of the empty array, e.g., (2, 3) or 2.</param>
/// <returns>Array of fill_value with the given shape, dtype, and order.</returns>
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html</remarks>
public static NDArray full(ValueType fill_value, Shape shape)
{
return(new NDArray(new UnmanagedStorage(ArraySlice.Allocate(fill_value.GetType(), shape.size, fill_value), shape)));
}